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HPE Machine Learning Development Environment Software
Are your model development and MLOps teams spending more time setting up and managing ML infrastructure, rather than building and deploying models to production?
HPE Machine Learning Development Environment as a managed service is a fully managed MLOps platform that allows model developers and researchers to focus on building better models faster by reducing complexity and removing the need to write boilerplate code associated with managing ML infrastructure. It easily integrates with ML frameworks and tools, and supports customers bringing their own AWS or GCP cloud environments.
Our platform also makes it easy for IT and MLOps teams to setup and share AI infrastructure to improve collaboration and productivity for ML teams, while reducing costs.
Train models faster, build more accurate models, efficiently manage and share AI infrastructure, and track and reproduce experiments easily with HPE Machine Learning Development Environment as a managed service.
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主要功能
Train Models Faster with Cutting-Edge Distributed Training Strategies and Techniques
HPE Machine Learning Development Environment Software integrates with DeepSpeed for 3D-Parallel (data-, model-, and pipeline-parallel) distributed training, to speed up training of large models like GPT-NeoX.
Enable Horovod for easy-to-use data-parallel distributed training.
Provide PyTorch Distributed Data Parallel (DDP) for flexibility and choice of distributed training strategies.
Find Better Model Configurations Efficiently with Cutting-Edge Hyperparameter Tuning Techniques
HPE Machine Learning Development Environment Software features production-grade implementation from the creators of the Asynchronous Successive Halving (ASHA) Hyperband algorithm for HPE search and optimization.
Define your own logic to coordinate across multiple trials within an experiment.
Implement your own custom hyperparameter search algorithms, ensembling, active learning, neural architecture search, and reinforcement learning.
Easily Share GPUs and Accelerators with ML Workflow-Aware Smart Scheduling and Resource Management
With HPE Machine Learning Development Environment Software, you can easily share your on-premises or cloud GPUs and accelerators with your ML development and operations teams.
Run ML and HPC jobs alongside each other on the same cluster, with support for workload managers like Slurm or PBS, and secure container runtimes like Singularity/Apptainer, Podman, or NVIDIA® Enroot.
Seamlessly use spot or preemptible instances to manage cloud costs.
Train models on NVIDIA or AMD GPUs without any code changes, with foundational support for accelerator heterogeneity.
Consistent user experience for deployments on your laptop to a supercomputer, and everything in-between including: baremetal, virtual machine (including cloud and on-premises IaaS solutions), Kubernetes, Slurm, and PBS.
Track and Reproduce your Work with Integrated Experiment Tracking and the Model Registry
HPE Machine Learning Development Environment Software provides built-in experiment tracking that covers model code, configuration, hyperparameters, metrics, and checkpoints.
Version, annotate, and organize trained models so that MLOps teams can effectively collaborate with model developers to manage your models' lifecycle.
規格速覽
HPE Cray AI 開發環境
AMD is a trademark of Advanced Micro Devices, Inc. GCP is a registered trademark of Google LLC. Linux is the registered trademark of Linus Torvalds in the U.S. and other countries. NVIDIA is a trademark and/or registered trademark of NVIDIA Corporation in the U.S. and other countries. Red Hat is a registered trademark of Red Hat, Inc. in the United States and other countries. All third-party marks are property of their respective owners.
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